Best AI Video Generation Platform in 2024

In this comprehensive review, John Draper evaluates four leading AI video generation platforms: RunwayML, Pabs, Decoherence (now known as Deoh), and Leonard.a. Each platform is tested using various images to assess their capabilities in generating landscapes, characters, and different art styles. Draper discusses the pros and cons of each, focusing on ease of use, output quality, and the ability to bring scenes and characters to life. RunwayML is praised for its cinematic shots and the innovative motion brush tool, while Pabs impresses with its negative prompting and canvas expansion features. Deoh offers a unique near-instant image generation with its turbo model, and Leonard.a shows promise with its stable video integration. Draper concludes that while RunwayML and Pabs are currently top contenders, the field is rapidly evolving with new technologies like stable video from Decoherence and Leonard.a.

Takeaways

• 🎬 The video compares four leading AI video generation platforms: RunwayML, Pabs, Decoherence (now Decohere), and Leonard.
• 🖼️ Decoherence offers three models: Flicker, Fluid, and Turbo, with the Turbo model featuring near-instant image generation and video conversion.
• 📹 Pabs (via their new P. Art website) provides a user-friendly UI, allowing for multiple generations, negative prompts, and creative editing features like expand canvas.
• 🌊 RunwayML is praised for its cinematic and landscape outputs, with powerful tools like motion brush and camera controls, although it tends to have a slow-motion feel.
• 📈 Pabs is noted for its potential to become the top platform once it introduces a paid tier to remove the watermark from its outputs.
• 🔍 The video demonstrates how to use each platform, including uploading images, adding text prompts, and adjusting motion settings for video generation.
• 🦕 Leonard.ai, new to the market, shows promise with its motion feature built on stable diffusion, although currently limited in comparison to other platforms.
• 📚 The script provides detailed steps on how to use each platform's specific features, such as modifying regions and expanding canvas in Pabs, and motion brush in RunwayML.
• 🤖 AI video generation platforms are not perfect, with challenges in character animation and maintaining detail in complex scenes, but they are improving rapidly.
• 📈 Topaz Video AI is mentioned as a tool for upscaling AI video generations to higher resolutions, with impressive results.
• 📊 The video concludes that while RunwayML and Pabs are currently leading, the field is competitive and evolving, with new entrants like Leonard.ai showing potential.
• ✅ The presenter encourages viewers to share their thoughts on which platform they prefer and what features they would like to see in future updates.

Q & A

What are the four AI video generation platforms discussed in the video?

The four AI video generation platforms discussed are RunwayML, Pabs, Decoherence (also known as Deoh), and Leonard.

What is the unique feature of Decoherence's Turbo model?

The unique feature of Decoherence's Turbo model is its near-instant image generation, which allows users to type in a text prompt, generate images, and then convert those into a video file with just a button press.

How does the video creator suggest improving the resolution of the video files generated by Decoherence?

The video creator suggests upscaling the low-resolution videos to 4K using a tool like Topaz Video AI.

What is the main advantage of Pabs' new UI?

The main advantage of Pabs' new UI is its ease of use and organization, which allows users to create multiple generations, use negative text prompts, and perform image editing and canvas expansion to refine the output.

How does Runway ML's Gen 2 model differ from its Gen 1 model?

Runway ML's Gen 2 model has improved video generation capabilities with better motion control and higher output resolution compared to Gen 1.

What is the motion brush feature in Runway ML?

The motion brush feature in Runway ML allows users to paint into their scene to specify areas that should be animated, giving extra control over the AI model's output.

What is the main challenge that Runway ML faces with character animations?

Runway ML struggles with maintaining character consistency, especially with 2D characters, and often produces a slow-motion feel even when motion settings are increased.

How does Leonard's motion feature work?

Leonard's motion feature works by first generating an image using the platform's image generation tool, then applying motion to that image through the motion tool, which allows for camera movement and some animation of the image content.

What is the potential improvement that the video creator hopes to see in Runway ML in the future?

The video creator hopes for the implementation of negative prompts in the future of Runway ML and the ability to apply multiple motion brush passes to a clip.

Which tool is mentioned for upscaling AI-generated videos to higher resolutions?

Topaz Video AI is mentioned as a tool for upscaling AI-generated videos to higher resolutions, such as 4K.

What is the current limitation of the stable video technology used by Decoherence and Leonard?

The current limitation of the stable video technology is that it is slightly limited in what it can do at the moment, but it has the potential to become a strong contender as future updates are released, allowing for longer content creation and more detailed direction.

Outlines

00:00

🎬 Introduction to AI Video Generation Platforms

The video begins with the host introducing the topic of AI video generation platforms. They plan to test and compare four leading platforms: RunwayML, Pabs (previously known as Decoh), Decoherence (referred to as Deoh), and Leonard. The host intends to use various images to explore landscapes, characters, and art styles, and will evaluate the platforms based on their ability to handle different scenes, character animations, ease of use, and output quality.

05:01

🖼️ Testing Decoherence (Deoh) with Anime Style Ninja Warrior

The host first tests Decoherence, now known as Deoh, which offers three models: Flicker, Fluid, and Turbo. They discuss the unique feature of near-instant image generation with the Turbo model. The host uses a pre-prepared anime-style Ninja Warrior image to test the platform's motion settings and compares the outputs at different motion levels. They also test the platform's ability to convert an image generated on Deoh's platform into a video file.

10:01

🐾 Exploring Pabs with a Polar Bear Image

The host moves on to Pabs, which has a user interface that allows for multiple generations and organization of outputs. They demonstrate how to use positive and negative text prompts, camera guidance, and motion control to generate a video of a polar bear walking in New York. The host also explores the 'modify region' and 'expand canvas' features, which enable the addition of new elements to an image and the extension of the canvas to create more content.

15:02

🌊 Runway ML's Gen 2 for Video Generation

The host discusses Runway ML, highlighting its various AI tools for image creation, background removal, and motion tracking. They focus on Gen 2, the platform's latest video generation model, which allows for text prompt-based image generation and camera motion control. The host demonstrates the motion brush feature, which provides extra control over the animation in specific areas of the scene.

20:03

🦕 Testing Leonardo's Stable Diffusion-Powered Motion Feature

The host explores Leonardo's new motion tool, which is powered by stable diffusion. They show how to use the real-time canvas for image generation and then convert those images to motion. The host tests the tool with various images, noting the camera movement and animation of subjects like a dinosaur, and discusses the potential for future improvements as stable diffusion technology advances.

25:04

📚 Conclusion and Upscaling with Topaz Video AI

In conclusion, the host states that Runway ML and Pabs are currently the top platforms for AI video generation, with Pabs slightly ahead due to its user interface and features like negative prompting and canvas expansion. They also mention the potential of stable video platforms like Decoherence and Leonardo. The host demonstrates using Topaz Video AI to upscale AI-generated videos to higher resolutions, enhancing the quality for professional use.

Keywords

AI video generation

AI video generation refers to the use of artificial intelligence to automatically create videos. In the video, this technology is central as the host compares different platforms that utilize AI to generate videos from images, art styles, and text prompts. It is exemplified by the platforms like RunwayML, Pabs, Decoherence, and Leonard, which are evaluated based on their ability to animate landscapes, characters, and the overall quality of the output.

RunwayML

RunwayML is one of the leading AI video generation platforms mentioned in the video. It is highlighted for its advanced tools, such as the motion brush and camera controls, which allow for detailed animation and video creation. The host discusses using RunwayML to generate videos with cinematic shots and landscapes, noting its strengths in these areas compared to other platforms.

Pabs

Pabs, also known as P. Art, is another AI video generation platform that the host examines. It is noted for its user interface and features like negative prompting and the ability to modify regions and expand canvas, which help refine the output and create more detailed animations. The platform is currently free to use and is seen as a strong contender against RunwayML.

Decoherence

Decoherence, now referred to as Decohere, is an AI video generation platform that the host tests for its new motion ability. It is known for its flicker, fluid, and turbo models, which generate different styles of video outputs. The host discusses the ease of use and the unique feature of near-instant image generation that can be quickly converted into a video file.

Leonardo

Leonardo is a newer platform to the AI video generation market with its stable diffusion-powered motion feature. The host explores its capabilities, noting that while it is currently limited, it has a lot of promise. It is built on stable video technology, which is expected to improve and offer more control and extended clip generation in the future.

Text prompts

Text prompts are a method used within AI video generation platforms to guide the AI in creating specific content. The host uses text prompts to generate images and videos with themes such as 'pirate ship rocking in giant waves' or 'polar bear walking in New York Street.' These prompts are critical in directing the AI to produce desired animations and scenes.

Negative prompting

Negative prompting is a feature in some AI video generation platforms that allows users to input what they do not want in the generated content. For example, the host uses negative prompts like 'ugly,' 'bad,' 'terrible,' 'too many legs,' and 'odd legs' to avoid unwanted features in the generated videos. This helps refine the output to better match the user's vision.

Camera controls

Camera controls in AI video generation platforms enable users to dictate the movement and perspective of the video. The host discusses how platforms like RunwayML and Pabs offer sophisticated camera controls that allow for panning, zooming, and 3D movement around the subject, which contribute to the realism and cinematic quality of the generated videos.

Stable video

Stable video refers to a technology used within AI video generation platforms that ensures the generated videos are fluid and stable. The host mentions that platforms like Decohere and Leonardo utilize stable video, which is in its early stages but expected to improve with future updates, allowing for higher quality and longer video generation.

Upscaling

Upscaling is the process of increasing the resolution of a video or image. The host discusses using Topaz Video AI, a paid tool, to upscale AI-generated videos from low resolutions to 4K or higher. This is particularly useful for enhancing the quality of AI-generated content for professional use.

Motion brush

The motion brush is a feature in RunwayML that allows users to 'paint' motion into specific parts of the video. The host finds this tool particularly useful for controlling the animation in generated videos, giving users more creative control over the final output.

Highlights

The video compares four leading AI video generation platforms: RunwayML, Pabs, Decoherence, and Leonard.

Decoherence, now known as Decohere, offers three models: Flicker, Fluid, and Turbo, with unique features like instant image generation.

Decohere's Turbo model utilizes stable video for quick conversion of text prompts into video files.

Pabs (P5) is praised for its user interface, negative prompting feature, and the ability to refine video outputs.

Pabs allows users to modify regions and expand canvas, providing more creative control over generated content.

RunwayML is recognized for its cinematic outputs and powerful tools like motion brush and camera controls.

RunwayML's Gen 2 model offers improved video generation capabilities over its predecessor.

Leonardo.ai's motion feature, powered by stable diffusion, shows promise despite current limitations.

Topaz Video AI is showcased as a tool for upscaling AI-generated videos to higher resolutions.

The video discusses the potential for future improvements in AI video generation platforms, including character animation and motion control.

Each platform's approach to motion and animation is evaluated, with examples of successful and less successful outputs.

The reviewer suggests that Pabs might soon surpass RunwayML in terms of platform capabilities.

AI video generation platforms are assessed based on their ability to handle landscapes, character animations, and art styles.

The ease of use and overall quality of output are critical factors in evaluating the platforms.

The video provides a detailed look at the process of generating videos using each platform, including the use of text prompts and image uploads.

The reviewer highlights the importance of conducting multiple generations to achieve the best results from each platform.

The potential integration of negative prompts and motion brush passes in future updates of RunwayML is discussed.

The video concludes by acknowledging the rapid advancements in AI video generation and the competition among platforms to offer the best tools.